Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.

With the constant exploration and development of intelligent manufacturing, the concept of digital twins has been proposed and applied. In view of the complexity and intellectualization of virtual workshop systems, real workshops can link with virtual workshosp based on AR under the structure of digital twins, which allows users to interact with virtual information and perceive the virtual information superimposed on the real world with great immersion. However, the three-dimensionality of virtual workshops and interaction with complex workshop information can be challenging for users. Due to a shortage of input bandwidth and the nontraditional mode of interaction, a more natural interaction technique for virtual workshops is required. To solve such problems, this paper presents a technical framework for 3D eye movement interaction applied to a virtual workshop. An eye movement interaction technique, oriented to implicit interaction and explicit interaction, is developed by establishing behavior recognition and interaction intention understanding. An eye-movement experiment verifies the former’s accuracy is above 90% and had better recognition performance. A better feature vector group of the latter is selected to establish a model and verify its feasibility and effectiveness. Finally, the feasibility of the framework is verified through the development of an application example.